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Found 27 papers, 4 papers with code
DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning
We implement a baseline by applying cylindrical rectification on the fisheye images and using a standard LSS-based BEV segmentation model.
Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks
It is essential to prove that lossy video compression artifacts do not impact the performance of the perception algorithms.
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving
In particular, our method estimates dense and accurate 3D structures and creates an implicit map representation based on signed distance fields, which can be further rendered into RGB images, and depth maps.
Multi-camera Bird's Eye View Perception for Autonomous Driving
Surround vision systems that are pretty common in new vehicles use the IPM principle to generate a BEV image and to show it on display to the driver.
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection.
X$^3$KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Specifically, we propose cross-task distillation from an instance segmentation teacher (X-IS) in the PV feature extraction stage providing supervision without ambiguous error backpropagation through the view transformation.
Ranked #5 on
3D Object Detection
on nuscenes Camera-Radar
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e. g., drivable area, in autonomous driving.
Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge
Object detection is a comprehensively studied problem in autonomous driving.
Surround-View Cameras based Holistic Visual Perception for Automated Driving
The human eye is one of the most sophisticated developments of evolution, but it still has defects.
Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges
Surround-view fisheye cameras are commonly used for near-field sensing in automated driving.
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it.
Detecting Adversarial Perturbations in Multi-Task Perception
In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception of complex vision tasks (i. e., depth estimation and semantic segmentation).
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving
In this paper, we present a system for incrementally reconstructing a dense 3D model of the geometry of an outdoor environment using a single monocular camera attached to a moving vehicle.
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
On the other hand, current deep neural networks are easily fooled by adversarial attacks.
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad.
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning
There is no public dataset for weather and light level classification focused on autonomous driving to the best of our knowledge.
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches.
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks.
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline
It is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios to the best of our knowledge.
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images.
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
To further illustrate the general applicability of the proposed framework, we apply it to wide-angle fisheye cameras with 190$^\circ$ horizontal field of view.
TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
Localized detection of soiling in an image is necessary to control the cleaning system.
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
We propose a novel GAN based algorithm for generating unseen patterns of soiled images.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view ($>180^{\circ}$).
FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
In this work, we propose a CNN architecture for moving object detection using fisheye images that were captured in autonomous driving environment.
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
For instance, they do not capture the extensive variability in the appearance of objects like vehicles present in real datasets.
